Overview

Dataset statistics

Number of variables23
Number of observations52
Missing cells547
Missing cells (%)45.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory186.5 B

Variable types

Numeric14
Unsupported6
Categorical3

Alerts

Year is highly overall correlated with Oil and 10 other fieldsHigh correlation
Oil is highly overall correlated with Year and 10 other fieldsHigh correlation
Total Primaries is highly overall correlated with Year and 10 other fieldsHigh correlation
Electricity is highly overall correlated with Coal and 1 other fieldsHigh correlation
LPG is highly overall correlated with Coal and 1 other fieldsHigh correlation
Gasoline/alcohol is highly overall correlated with Diesel oil and 7 other fieldsHigh correlation
Kerosene/jet fuel is highly overall correlated with Year and 11 other fieldsHigh correlation
Diesel oil is highly overall correlated with Gasoline/alcohol and 8 other fieldsHigh correlation
Fuel oil is highly overall correlated with Year and 10 other fieldsHigh correlation
Charcoal is highly overall correlated with Oil and 6 other fieldsHigh correlation
Other secondary is highly overall correlated with Year and 8 other fieldsHigh correlation
Non-energy is highly overall correlated with Year and 10 other fieldsHigh correlation
Total Secundaries is highly overall correlated with Year and 12 other fieldsHigh correlation
Total is highly overall correlated with Year and 11 other fieldsHigh correlation
Coal is highly overall correlated with Year and 13 other fieldsHigh correlation
Coke is highly overall correlated with Year and 14 other fieldsHigh correlation
Gases is highly overall correlated with Year and 7 other fieldsHigh correlation
Oil has 12 (23.1%) missing valuesMissing
Natural gas has 52 (100.0%) missing valuesMissing
Coal has 49 (94.2%) missing valuesMissing
Hydroenergy has 52 (100.0%) missing valuesMissing
Nuclear has 52 (100.0%) missing valuesMissing
Firewood has 52 (100.0%) missing valuesMissing
Sugarcane and products has 52 (100.0%) missing valuesMissing
Other Primary_x000d_ has 52 (100.0%) missing valuesMissing
Total Primaries has 11 (21.2%) missing valuesMissing
Electricity has 2 (3.8%) missing valuesMissing
LPG has 6 (11.5%) missing valuesMissing
Gasoline/alcohol has 1 (1.9%) missing valuesMissing
Coke has 48 (92.3%) missing valuesMissing
Charcoal has 39 (75.0%) missing valuesMissing
Gases has 37 (71.2%) missing valuesMissing
Other secondary has 24 (46.2%) missing valuesMissing
Non-energy has 6 (11.5%) missing valuesMissing
Year is uniformly distributedUniform
Coal is uniformly distributedUniform
Coke is uniformly distributedUniform
Year has unique valuesUnique
Kerosene/jet fuel has unique valuesUnique
Diesel oil has unique valuesUnique
Fuel oil has unique valuesUnique
Total Secundaries has unique valuesUnique
Total has unique valuesUnique
Natural gas is an unsupported type, check if it needs cleaning or further analysisUnsupported
Hydroenergy is an unsupported type, check if it needs cleaning or further analysisUnsupported
Nuclear is an unsupported type, check if it needs cleaning or further analysisUnsupported
Firewood is an unsupported type, check if it needs cleaning or further analysisUnsupported
Sugarcane and products is an unsupported type, check if it needs cleaning or further analysisUnsupported
Other Primary_x000d_ is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-07-30 07:29:41.452881
Analysis finished2023-07-30 07:30:30.923505
Duration49.47 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Year
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1995.5
Minimum1970
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:30:31.144487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1972.55
Q11982.75
median1995.5
Q32008.25
95-th percentile2018.45
Maximum2021
Range51
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation15.154757
Coefficient of variation (CV)0.0075944662
Kurtosis-1.2
Mean1995.5
Median Absolute Deviation (MAD)13
Skewness0
Sum103766
Variance229.66667
MonotonicityStrictly increasing
2023-07-30T07:30:31.584361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1970 1
 
1.9%
1971 1
 
1.9%
1998 1
 
1.9%
1999 1
 
1.9%
2000 1
 
1.9%
2001 1
 
1.9%
2002 1
 
1.9%
2003 1
 
1.9%
2004 1
 
1.9%
2005 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1970 1
1.9%
1971 1
1.9%
1972 1
1.9%
1973 1
1.9%
1974 1
1.9%
1975 1
1.9%
1976 1
1.9%
1977 1
1.9%
1978 1
1.9%
1979 1
1.9%
ValueCountFrequency (%)
2021 1
1.9%
2020 1
1.9%
2019 1
1.9%
2018 1
1.9%
2017 1
1.9%
2016 1
1.9%
2015 1
1.9%
2014 1
1.9%
2013 1
1.9%
2012 1
1.9%

Oil
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)100.0%
Missing12
Missing (%)23.1%
Infinite0
Infinite (%)0.0%
Mean17460.888
Minimum30.47
Maximum71982.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:30:32.004335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30.47
5-th percentile60.879
Q1742.055
median9082.94
Q327435.028
95-th percentile64552.15
Maximum71982.06
Range71951.59
Interquartile range (IQR)26692.972

Descriptive statistics

Standard deviation21470.304
Coefficient of variation (CV)1.2296227
Kurtosis0.38929705
Mean17460.888
Median Absolute Deviation (MAD)8996.47
Skewness1.1922487
Sum698435.51
Variance4.6097394 × 108
MonotonicityNot monotonic
2023-07-30T07:30:32.302187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
65.05 1
 
1.9%
28095.71 1
 
1.9%
12549.29 1
 
1.9%
14134.11 1
 
1.9%
19091.61 1
 
1.9%
21759.04 1
 
1.9%
22367.06 1
 
1.9%
27049.92 1
 
1.9%
32985.75 1
 
1.9%
31541.82 1
 
1.9%
Other values (30) 30
57.7%
(Missing) 12
 
23.1%
ValueCountFrequency (%)
30.47 1
1.9%
52.12 1
1.9%
61.34 1
1.9%
65.05 1
1.9%
107.89 1
1.9%
135.69 1
1.9%
190.61 1
1.9%
246.25 1
1.9%
674.34 1
1.9%
702.08 1
1.9%
ValueCountFrequency (%)
71982.06 1
1.9%
65724.07 1
1.9%
64490.47 1
1.9%
56589.39 1
1.9%
54715.51 1
1.9%
44490.35 1
1.9%
38639.33 1
1.9%
32985.75 1
1.9%
31541.82 1
1.9%
28095.71 1
1.9%

Natural gas
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Coal
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct3
Distinct (%)100.0%
Missing49
Missing (%)94.2%
Memory size548.0 B
48.98
0.44
40.37

Length

Max length5
Median length5
Mean length4.6666667
Min length4

Characters and Unicode

Total characters14
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row48.98
2nd row0.44
3rd row40.37

Common Values

ValueCountFrequency (%)
48.98 1
 
1.9%
0.44 1
 
1.9%
40.37 1
 
1.9%
(Missing) 49
94.2%

Length

2023-07-30T07:30:32.542894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T07:30:32.780108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
48.98 1
33.3%
0.44 1
33.3%
40.37 1
33.3%

Most occurring characters

ValueCountFrequency (%)
4 4
28.6%
. 3
21.4%
8 2
14.3%
0 2
14.3%
9 1
 
7.1%
3 1
 
7.1%
7 1
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11
78.6%
Other Punctuation 3
 
21.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 4
36.4%
8 2
18.2%
0 2
18.2%
9 1
 
9.1%
3 1
 
9.1%
7 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 4
28.6%
. 3
21.4%
8 2
14.3%
0 2
14.3%
9 1
 
7.1%
3 1
 
7.1%
7 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 4
28.6%
. 3
21.4%
8 2
14.3%
0 2
14.3%
9 1
 
7.1%
3 1
 
7.1%
7 1
 
7.1%

Hydroenergy
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Nuclear
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Firewood
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Sugarcane and products
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Other Primary_x000d_
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Total Primaries
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)100.0%
Missing11
Missing (%)21.2%
Infinite0
Infinite (%)0.0%
Mean17037.202
Minimum30.47
Maximum71982.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:30:33.000403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30.47
5-th percentile52.12
Q1702.08
median5786.8
Q327214.8
95-th percentile64490.47
Maximum71982.06
Range71951.59
Interquartile range (IQR)26512.72

Descriptive statistics

Standard deviation21374.596
Coefficient of variation (CV)1.2545837
Kurtosis0.47243034
Mean17037.202
Median Absolute Deviation (MAD)5756.33
Skewness1.2249985
Sum698525.3
Variance4.5687335 × 108
MonotonicityNot monotonic
2023-07-30T07:30:33.247525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
65.05 1
 
1.9%
28095.71 1
 
1.9%
12549.29 1
 
1.9%
14134.55 1
 
1.9%
19091.61 1
 
1.9%
21759.04 1
 
1.9%
22367.06 1
 
1.9%
27049.92 1
 
1.9%
32985.75 1
 
1.9%
31582.19 1
 
1.9%
Other values (31) 31
59.6%
(Missing) 11
 
21.2%
ValueCountFrequency (%)
30.47 1
1.9%
48.98 1
1.9%
52.12 1
1.9%
61.34 1
1.9%
65.05 1
1.9%
107.89 1
1.9%
135.69 1
1.9%
190.61 1
1.9%
246.25 1
1.9%
674.34 1
1.9%
ValueCountFrequency (%)
71982.06 1
1.9%
65724.07 1
1.9%
64490.47 1
1.9%
56589.39 1
1.9%
54715.51 1
1.9%
44490.35 1
1.9%
38639.33 1
1.9%
32985.75 1
1.9%
31582.19 1
1.9%
28095.71 1
1.9%

Electricity
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)76.0%
Missing2
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean23.9984
Minimum0.07
Maximum218.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:30:33.500284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.466
Q10.69
median2.195
Q321.8325
95-th percentile119.892
Maximum218.24
Range218.17
Interquartile range (IQR)21.1425

Descriptive statistics

Standard deviation45.346925
Coefficient of variation (CV)1.8895812
Kurtosis8.4970151
Mean23.9984
Median Absolute Deviation (MAD)1.87
Skewness2.8630894
Sum1199.92
Variance2056.3436
MonotonicityNot monotonic
2023-07-30T07:30:33.738390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.69 6
 
11.5%
0.6 5
 
9.6%
1.72 2
 
3.8%
40.2 2
 
3.8%
2.15 2
 
3.8%
92.65 1
 
1.9%
13.74 1
 
1.9%
24.28 1
 
1.9%
33.91 1
 
1.9%
174.51 1
 
1.9%
Other values (28) 28
53.8%
(Missing) 2
 
3.8%
ValueCountFrequency (%)
0.07 1
 
1.9%
0.22 1
 
1.9%
0.43 1
 
1.9%
0.51 1
 
1.9%
0.52 1
 
1.9%
0.6 5
9.6%
0.69 6
11.5%
0.77 1
 
1.9%
0.86 1
 
1.9%
0.95 1
 
1.9%
ValueCountFrequency (%)
218.24 1
1.9%
174.51 1
1.9%
129.72 1
1.9%
107.88 1
1.9%
92.65 1
1.9%
59.12 1
1.9%
44.44 1
1.9%
40.2 2
3.8%
33.91 1
1.9%
31.81 1
1.9%

LPG
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45
Distinct (%)97.8%
Missing6
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean27.748043
Minimum0.12
Maximum108.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:30:33.978320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.255
Q14.615
median17.935
Q346.6425
95-th percentile80.795
Maximum108.36
Range108.24
Interquartile range (IQR)42.0275

Descriptive statistics

Standard deviation27.666945
Coefficient of variation (CV)0.99707733
Kurtosis0.63043244
Mean27.748043
Median Absolute Deviation (MAD)15.92
Skewness1.1142047
Sum1276.41
Variance765.45986
MonotonicityNot monotonic
2023-07-30T07:30:34.247810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.12 2
 
3.8%
23.96 1
 
1.9%
41.16 1
 
1.9%
4.75 1
 
1.9%
108.36 1
 
1.9%
81.11 1
 
1.9%
79.85 1
 
1.9%
92.76 1
 
1.9%
20.72 1
 
1.9%
14.14 1
 
1.9%
Other values (35) 35
67.3%
(Missing) 6
 
11.5%
ValueCountFrequency (%)
0.12 2
3.8%
0.23 1
1.9%
0.33 1
1.9%
0.55 1
1.9%
1.05 1
1.9%
2.98 1
1.9%
3 1
1.9%
3.73 1
1.9%
3.75 1
1.9%
4.54 1
1.9%
ValueCountFrequency (%)
108.36 1
1.9%
92.76 1
1.9%
81.11 1
1.9%
79.85 1
1.9%
70.64 1
1.9%
62.66 1
1.9%
55.04 1
1.9%
50.14 1
1.9%
47.33 1
1.9%
47.05 1
1.9%

Gasoline/alcohol
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51
Distinct (%)100.0%
Missing1
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean1901.521
Minimum2.59
Maximum4772.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:30:34.501874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.59
5-th percentile68.19
Q11027.915
median1676.55
Q32853.88
95-th percentile3888.965
Maximum4772.95
Range4770.36
Interquartile range (IQR)1825.965

Descriptive statistics

Standard deviation1302.2651
Coefficient of variation (CV)0.68485442
Kurtosis-0.74071783
Mean1901.521
Median Absolute Deviation (MAD)1043.47
Skewness0.37768988
Sum96977.57
Variance1695894.3
MonotonicityNot monotonic
2023-07-30T07:30:34.773366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.59 1
 
1.9%
1414.49 1
 
1.9%
1436.8 1
 
1.9%
1676.55 1
 
1.9%
2434.82 1
 
1.9%
3042.38 1
 
1.9%
2456.29 1
 
1.9%
2720.02 1
 
1.9%
3520.82 1
 
1.9%
3911.9 1
 
1.9%
Other values (41) 41
78.8%
ValueCountFrequency (%)
2.59 1
1.9%
25 1
1.9%
25.52 1
1.9%
110.86 1
1.9%
130.57 1
1.9%
204.5 1
1.9%
244.5 1
1.9%
398.42 1
1.9%
438.66 1
1.9%
579.77 1
1.9%
ValueCountFrequency (%)
4772.95 1
1.9%
4726.19 1
1.9%
3911.9 1
1.9%
3866.03 1
1.9%
3778.14 1
1.9%
3708.7 1
1.9%
3684.24 1
1.9%
3564.37 1
1.9%
3520.82 1
1.9%
3102.11 1
1.9%

Kerosene/jet fuel
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean987.37942
Minimum8.19
Maximum2788.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:30:35.033572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8.19
5-th percentile51.475
Q1497.3
median758.295
Q31314.06
95-th percentile2406.1275
Maximum2788.92
Range2780.73
Interquartile range (IQR)816.76

Descriptive statistics

Standard deviation769.97789
Coefficient of variation (CV)0.77981967
Kurtosis-0.43851206
Mean987.37942
Median Absolute Deviation (MAD)418.725
Skewness0.78476651
Sum51343.73
Variance592865.96
MonotonicityNot monotonic
2023-07-30T07:30:35.281416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122.91 1
 
1.9%
48.34 1
 
1.9%
902.52 1
 
1.9%
869.01 1
 
1.9%
707.04 1
 
1.9%
639.33 1
 
1.9%
756.96 1
 
1.9%
1145.11 1
 
1.9%
1115.24 1
 
1.9%
1111.87 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
8.19 1
1.9%
34.4 1
1.9%
48.34 1
1.9%
54.04 1
1.9%
64.73 1
1.9%
96.67 1
1.9%
122.91 1
1.9%
158.06 1
1.9%
198.96 1
1.9%
212.06 1
1.9%
ValueCountFrequency (%)
2788.92 1
1.9%
2448.47 1
1.9%
2433.38 1
1.9%
2383.83 1
1.9%
2327.66 1
1.9%
2230.13 1
1.9%
2228.23 1
1.9%
2203.79 1
1.9%
2162.81 1
1.9%
1971.97 1
1.9%

Diesel oil
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean721.72096
Minimum47.15
Maximum1699.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:30:35.531307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum47.15
5-th percentile201.2335
Q1505.5925
median675.76
Q3894.045
95-th percentile1347.1985
Maximum1699.88
Range1652.73
Interquartile range (IQR)388.4525

Descriptive statistics

Standard deviation359.99063
Coefficient of variation (CV)0.49879476
Kurtosis0.24858771
Mean721.72096
Median Absolute Deviation (MAD)187.69
Skewness0.59852985
Sum37529.49
Variance129593.26
MonotonicityNot monotonic
2023-07-30T07:30:35.800309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.15 1
 
1.9%
135.46 1
 
1.9%
464.4 1
 
1.9%
521.14 1
 
1.9%
660.53 1
 
1.9%
741.66 1
 
1.9%
681.3 1
 
1.9%
694.85 1
 
1.9%
816.36 1
 
1.9%
973.63 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
47.15 1
1.9%
135.46 1
1.9%
166.05 1
1.9%
230.02 1
1.9%
263.89 1
1.9%
264.91 1
1.9%
346.36 1
1.9%
349.79 1
1.9%
367.11 1
1.9%
456.1 1
1.9%
ValueCountFrequency (%)
1699.88 1
1.9%
1526.08 1
1.9%
1384.34 1
1.9%
1316.81 1
1.9%
1280.18 1
1.9%
1276.12 1
1.9%
1208.7 1
1.9%
1130.97 1
1.9%
1123.44 1
1.9%
1004.52 1
1.9%

Fuel oil
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4600.0538
Minimum313.88
Maximum13572.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:30:36.062839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum313.88
5-th percentile560.4425
Q11923.16
median2596.365
Q37884.195
95-th percentile9244.763
Maximum13572.51
Range13258.63
Interquartile range (IQR)5961.035

Descriptive statistics

Standard deviation3486.1491
Coefficient of variation (CV)0.75784964
Kurtosis-0.83968282
Mean4600.0538
Median Absolute Deviation (MAD)1976.105
Skewness0.56216424
Sum239202.8
Variance12153236
MonotonicityNot monotonic
2023-07-30T07:30:36.340518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
748.34 1
 
1.9%
313.88 1
 
1.9%
3921.16 1
 
1.9%
4191.17 1
 
1.9%
5058.31 1
 
1.9%
6974.61 1
 
1.9%
7698.33 1
 
1.9%
8381.85 1
 
1.9%
9607.18 1
 
1.9%
8036.77 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
313.88 1
1.9%
425.72 1
1.9%
510.31 1
1.9%
601.46 1
1.9%
639.06 1
1.9%
684.61 1
1.9%
748.34 1
1.9%
934.27 1
1.9%
1143.22 1
1.9%
1600.28 1
1.9%
ValueCountFrequency (%)
13572.51 1
1.9%
11963.08 1
1.9%
9607.18 1
1.9%
8948.24 1
1.9%
8878.53 1
1.9%
8814.11 1
1.9%
8501.13 1
1.9%
8396.75 1
1.9%
8381.85 1
1.9%
8278.32 1
1.9%

Coke
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct4
Distinct (%)100.0%
Missing48
Missing (%)92.3%
Memory size548.0 B
0.69
0.89
1.03
0.41

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters16
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row0.69
2nd row0.89
3rd row1.03
4th row0.41

Common Values

ValueCountFrequency (%)
0.69 1
 
1.9%
0.89 1
 
1.9%
1.03 1
 
1.9%
0.41 1
 
1.9%
(Missing) 48
92.3%

Length

2023-07-30T07:30:36.571042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T07:30:36.807665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.69 1
25.0%
0.89 1
25.0%
1.03 1
25.0%
0.41 1
25.0%

Most occurring characters

ValueCountFrequency (%)
0 4
25.0%
. 4
25.0%
9 2
12.5%
1 2
12.5%
6 1
 
6.2%
8 1
 
6.2%
3 1
 
6.2%
4 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12
75.0%
Other Punctuation 4
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4
33.3%
9 2
16.7%
1 2
16.7%
6 1
 
8.3%
8 1
 
8.3%
3 1
 
8.3%
4 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4
25.0%
. 4
25.0%
9 2
12.5%
1 2
12.5%
6 1
 
6.2%
8 1
 
6.2%
3 1
 
6.2%
4 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4
25.0%
. 4
25.0%
9 2
12.5%
1 2
12.5%
6 1
 
6.2%
8 1
 
6.2%
3 1
 
6.2%
4 1
 
6.2%

Charcoal
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)92.3%
Missing39
Missing (%)75.0%
Infinite0
Infinite (%)0.0%
Mean8.0853846
Minimum3.3
Maximum18.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:30:37.005858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile4.092
Q16.37
median7.07
Q38.58
95-th percentile14.854
Maximum18.04
Range14.74
Interquartile range (IQR)2.21

Descriptive statistics

Standard deviation3.7998873
Coefficient of variation (CV)0.46996989
Kurtosis3.3583698
Mean8.0853846
Median Absolute Deviation (MAD)1.41
Skewness1.6341653
Sum105.11
Variance14.439144
MonotonicityNot monotonic
2023-07-30T07:30:38.956189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6.37 2
 
3.8%
12.73 1
 
1.9%
7.78 1
 
1.9%
7.07 1
 
1.9%
3.3 1
 
1.9%
6.61 1
 
1.9%
5.66 1
 
1.9%
4.62 1
 
1.9%
8.58 1
 
1.9%
18.04 1
 
1.9%
Other values (2) 2
 
3.8%
(Missing) 39
75.0%
ValueCountFrequency (%)
3.3 1
1.9%
4.62 1
1.9%
5.66 1
1.9%
6.37 2
3.8%
6.61 1
1.9%
7.07 1
1.9%
7.78 1
1.9%
8.38 1
1.9%
8.58 1
1.9%
9.6 1
1.9%
ValueCountFrequency (%)
18.04 1
1.9%
12.73 1
1.9%
9.6 1
1.9%
8.58 1
1.9%
8.38 1
1.9%
7.78 1
1.9%
7.07 1
1.9%
6.61 1
1.9%
6.37 2
3.8%
5.66 1
1.9%

Gases
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)33.3%
Missing37
Missing (%)71.2%
Memory size548.0 B
0.41
11 
0.43
 
1
3.59
 
1
4.28
 
1
6.89
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters60
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)26.7%

Sample

1st row0.43
2nd row3.59
3rd row4.28
4th row6.89
5th row0.41

Common Values

ValueCountFrequency (%)
0.41 11
 
21.2%
0.43 1
 
1.9%
3.59 1
 
1.9%
4.28 1
 
1.9%
6.89 1
 
1.9%
(Missing) 37
71.2%

Length

2023-07-30T07:30:39.179507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T07:30:39.408263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.41 11
73.3%
0.43 1
 
6.7%
3.59 1
 
6.7%
4.28 1
 
6.7%
6.89 1
 
6.7%

Most occurring characters

ValueCountFrequency (%)
. 15
25.0%
4 13
21.7%
0 12
20.0%
1 11
18.3%
3 2
 
3.3%
9 2
 
3.3%
8 2
 
3.3%
5 1
 
1.7%
2 1
 
1.7%
6 1
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45
75.0%
Other Punctuation 15
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 13
28.9%
0 12
26.7%
1 11
24.4%
3 2
 
4.4%
9 2
 
4.4%
8 2
 
4.4%
5 1
 
2.2%
2 1
 
2.2%
6 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 15
25.0%
4 13
21.7%
0 12
20.0%
1 11
18.3%
3 2
 
3.3%
9 2
 
3.3%
8 2
 
3.3%
5 1
 
1.7%
2 1
 
1.7%
6 1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 15
25.0%
4 13
21.7%
0 12
20.0%
1 11
18.3%
3 2
 
3.3%
9 2
 
3.3%
8 2
 
3.3%
5 1
 
1.7%
2 1
 
1.7%
6 1
 
1.7%

Other secondary
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)100.0%
Missing24
Missing (%)46.2%
Infinite0
Infinite (%)0.0%
Mean283.21643
Minimum18.57
Maximum841.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:30:39.634556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18.57
5-th percentile46.9455
Q1155.7
median236.66
Q3385.1575
95-th percentile581.544
Maximum841.83
Range823.26
Interquartile range (IQR)229.4575

Descriptive statistics

Standard deviation194.93293
Coefficient of variation (CV)0.68828257
Kurtosis1.1120255
Mean283.21643
Median Absolute Deviation (MAD)106.13
Skewness1.1193506
Sum7930.06
Variance37998.848
MonotonicityNot monotonic
2023-07-30T07:30:39.848801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
279.48 1
 
1.9%
567.19 1
 
1.9%
583.07 1
 
1.9%
554.04 1
 
1.9%
578.71 1
 
1.9%
406.52 1
 
1.9%
385.72 1
 
1.9%
384.97 1
 
1.9%
352.74 1
 
1.9%
278.02 1
 
1.9%
Other values (18) 18
34.6%
(Missing) 24
46.2%
ValueCountFrequency (%)
18.57 1
1.9%
26.18 1
1.9%
85.51 1
1.9%
98.63 1
1.9%
107.32 1
1.9%
140.48 1
1.9%
151.35 1
1.9%
157.15 1
1.9%
174.51 1
1.9%
181.73 1
1.9%
ValueCountFrequency (%)
841.83 1
1.9%
583.07 1
1.9%
578.71 1
1.9%
567.19 1
1.9%
554.04 1
1.9%
406.52 1
1.9%
385.72 1
1.9%
384.97 1
1.9%
352.74 1
1.9%
279.48 1
1.9%

Non-energy
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)95.7%
Missing6
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean271.81522
Minimum0.86
Maximum778.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:30:40.078311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.86
5-th percentile2.3975
Q1119.53
median182.54
Q3435.21
95-th percentile669.07
Maximum778.9
Range778.04
Interquartile range (IQR)315.68

Descriptive statistics

Standard deviation216.38757
Coefficient of variation (CV)0.79608335
Kurtosis-0.61663484
Mean271.81522
Median Absolute Deviation (MAD)117.79
Skewness0.73697791
Sum12503.5
Variance46823.579
MonotonicityNot monotonic
2023-07-30T07:30:40.337641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.86 3
 
5.8%
382.21 1
 
1.9%
476.36 1
 
1.9%
78.06 1
 
1.9%
124.28 1
 
1.9%
163.83 1
 
1.9%
437.61 1
 
1.9%
778.9 1
 
1.9%
623.28 1
 
1.9%
700.32 1
 
1.9%
Other values (34) 34
65.4%
(Missing) 6
 
11.5%
ValueCountFrequency (%)
0.86 3
5.8%
7.01 1
 
1.9%
40.31 1
 
1.9%
51.44 1
 
1.9%
78.06 1
 
1.9%
82.12 1
 
1.9%
90.89 1
 
1.9%
92.4 1
 
1.9%
116.75 1
 
1.9%
119.24 1
 
1.9%
ValueCountFrequency (%)
778.9 1
1.9%
700.32 1
1.9%
682.88 1
1.9%
627.64 1
1.9%
623.28 1
1.9%
604.21 1
1.9%
531 1
1.9%
526.39 1
1.9%
489.84 1
1.9%
487.08 1
1.9%

Total Secundaries
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8617.1404
Minimum502.68
Maximum19408.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:30:40.598486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum502.68
5-th percentile1189.258
Q14152.3675
median7311.935
Q313512.188
95-th percentile16876.212
Maximum19408.18
Range18905.5
Interquartile range (IQR)9359.82

Descriptive statistics

Standard deviation5423.7833
Coefficient of variation (CV)0.62941801
Kurtosis-1.2848625
Mean8617.1404
Median Absolute Deviation (MAD)5224.22
Skewness0.17720577
Sum448091.3
Variance29417426
MonotonicityNot monotonic
2023-07-30T07:30:40.864679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
920.12 1
 
1.9%
502.68 1
 
1.9%
6977.73 1
 
1.9%
7265.07 1
 
1.9%
8527.27 1
 
1.9%
11020.6 1
 
1.9%
12659.41 1
 
1.9%
13168.59 1
 
1.9%
15032.35 1
 
1.9%
14727.27 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
502.68 1
1.9%
920.12 1
1.9%
950.36 1
1.9%
1384.72 1
1.9%
1453.17 1
1.9%
1600.39 1
1.9%
1811.6 1
1.9%
2069.63 1
1.9%
2105.8 1
1.9%
2679.05 1
1.9%
ValueCountFrequency (%)
19408.18 1
1.9%
17331.46 1
1.9%
16974.91 1
1.9%
16795.46 1
1.9%
16109.3 1
1.9%
15306 1
1.9%
15032.35 1
1.9%
14999.1 1
1.9%
14730.15 1
1.9%
14727.27 1
1.9%

Total
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22050.319
Minimum985.17
Maximum91390.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:30:41.136827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum985.17
5-th percentile1825.96
Q14684.0125
median7327.17
Q338751.372
95-th percentile75134.158
Maximum91390.24
Range90405.07
Interquartile range (IQR)34067.36

Descriptive statistics

Standard deviation24581.911
Coefficient of variation (CV)1.1148097
Kurtosis0.70659656
Mean22050.319
Median Absolute Deviation (MAD)4825.395
Skewness1.2823916
Sum1146616.6
Variance6.0427033 × 108
MonotonicityNot monotonic
2023-07-30T07:30:41.390415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
985.17 1
 
1.9%
1269.02 1
 
1.9%
6977.73 1
 
1.9%
7295.54 1
 
1.9%
9500.11 1
 
1.9%
16807.4 1
 
1.9%
25038.49 1
 
1.9%
25906.28 1
 
1.9%
27581.65 1
 
1.9%
28861.81 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
985.17 1
1.9%
1269.02 1
1.9%
1453.17 1
1.9%
2130.97 1
1.9%
2352.05 1
1.9%
2651.5 1
1.9%
2763.2 1
1.9%
2913.16 1
1.9%
3484.41 1
1.9%
3533.3 1
1.9%
ValueCountFrequency (%)
91390.24 1
1.9%
83055.53 1
1.9%
79796.47 1
1.9%
71319.54 1
1.9%
66860.85 1
1.9%
56521.82 1
1.9%
52041.64 1
1.9%
46446.24 1
1.9%
45715.53 1
1.9%
42049.03 1
1.9%

Interactions

2023-07-30T07:30:25.726116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:42.283687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:46.871134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:50.241594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:54.035430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:56.896013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:59.756846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:02.822787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:06.772116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:09.775328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:12.750904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:15.691352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:19.577844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:22.661976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:26.150084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:42.762034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:47.227809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:50.812207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:54.427335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:57.291838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:00.165502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:03.481024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:07.193687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:10.232987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:12.950634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:16.116837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:19.972067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:23.077211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:26.356044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:43.069518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:47.429489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:51.153278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:54.615200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:57.504793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:00.363955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:03.810716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:07.391871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:10.436859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:13.162794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:16.436046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:20.191521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:23.286959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:26.557365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:43.383877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:47.623874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:51.485471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:54.806291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:57.696362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:00.575623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:04.138569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:07.589576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:10.636399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:13.366040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:16.754302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:20.411715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:23.512566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:26.725575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:43.698288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:47.803966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:51.807198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:54.976575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:57.861186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:00.736044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:04.441037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:07.787907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:10.821043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:13.547374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:17.054384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:20.600400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:23.698568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:26.897096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:44.015677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:47.987314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:52.137417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:55.147806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:58.037889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:00.911159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:04.747814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:07.986708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:11.012900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:13.732209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:17.357837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:20.787720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:23.884783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:27.073105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:44.348609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:48.178076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:52.370277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:55.332486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:58.205844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:01.086385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:05.075059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:08.209468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:11.196708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:13.946469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:17.646364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:20.990615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:24.083310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:27.256751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:44.675664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:48.379593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:52.562562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:55.516391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:58.389148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:01.267055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:05.386325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:08.395406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:11.380406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:14.141620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:17.869430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:21.191294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:24.299884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:27.469883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:45.031970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:48.577232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:52.760806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:55.712619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:58.595610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:01.461462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:05.575678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:08.594705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:11.571803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:14.331631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:18.181709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:21.419663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:24.512015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:27.653789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:45.371578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:48.771091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:52.979764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:55.915144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:58.788533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:01.663342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:05.773624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:08.802533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:11.756989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:14.548081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:18.508705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:21.628233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:24.725370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:27.853023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:45.581674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:49.042114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:53.193764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:56.106096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:58.986413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:01.859627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:05.984415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:09.003006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:11.975794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:14.738686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:18.821411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:21.851518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:24.928982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:28.025928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:45.844409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:49.272833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:53.406979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:56.301394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:59.163687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:02.023416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:06.168740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:09.178292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:12.171026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:14.974759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:18.987267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:22.025874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:25.116425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:28.233053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:46.170133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:49.590820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:53.623262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:56.511928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:59.378703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:02.212985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:06.373985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:09.393324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:12.376238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:15.204380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:19.192547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:22.246197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:25.344676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:28.463335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:46.550034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:49.932057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:53.843195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:56.733604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:59.594386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:02.525269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:06.586449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:09.603526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:12.580141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:15.410172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:19.413804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:22.458123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:25.550473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-30T07:30:41.607472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
YearOilTotal PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCharcoalOther secondaryNon-energyTotal SecundariesTotalCoalCokeGases
Year1.0000.8510.8350.172-0.4790.4590.9090.3730.8950.2200.8940.7990.8880.9321.0001.0001.000
Oil0.8511.0001.0000.355-0.4790.4630.7320.3570.7300.6500.8090.6810.7510.8841.0001.0000.000
Total Primaries0.8351.0001.0000.370-0.4720.3950.7290.3250.7210.6500.8090.6830.7390.8651.0001.0000.000
Electricity0.1720.3550.3701.000-0.0040.0830.1920.2970.1910.213-0.0550.3020.2300.2291.0001.0000.369
LPG-0.479-0.479-0.472-0.0041.0000.049-0.3010.213-0.2680.342-0.412-0.264-0.241-0.4081.0001.0000.237
Gasoline/alcohol0.4590.4630.3950.0830.0491.0000.4450.6460.6000.5940.0910.4190.7360.6021.0001.0000.509
Kerosene/jet fuel0.9090.7320.7290.192-0.3010.4451.0000.5200.8340.3050.6860.8520.8520.8901.0001.0000.642
Diesel oil0.3730.3570.3250.2970.2130.6460.5201.0000.4960.622-0.0650.5250.6390.5651.0001.0000.505
Fuel oil0.8950.7300.7210.191-0.2680.6000.8340.4961.0000.3800.5550.7590.9510.9071.0001.0000.000
Charcoal0.2200.6500.6500.2130.3420.5940.3050.6220.3801.0000.0490.5500.4180.407NaN1.0001.000
Other secondary0.8940.8090.809-0.055-0.4120.0910.686-0.0650.5550.0491.0000.4400.5510.8181.0001.0000.258
Non-energy0.7990.6810.6830.302-0.2640.4190.8520.5250.7590.5500.4401.0000.8210.8311.0001.0000.222
Total Secundaries0.8880.7510.7390.230-0.2410.7360.8520.6390.9510.4180.5510.8211.0000.9491.0001.0000.550
Total0.9320.8840.8650.229-0.4080.6020.8900.5650.9070.4070.8180.8310.9491.0001.0001.0000.000
Coal1.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.0001.0001.0001.000NaN1.000
Coke1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.000
Gases1.0000.0000.0000.3690.2370.5090.6420.5050.0001.0000.2580.2220.5500.0001.0001.0001.000

Missing values

2023-07-30T07:30:28.785598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-30T07:30:29.797991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-30T07:30:30.493662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

115YearOilNatural gasCoalHydroenergyNuclearFirewoodSugarcane and productsOther Primary_x000d_Total PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCokeCharcoalGasesOther secondaryNon-energyTotal SecundariesTotal
1197065.05NaNNaNNaNNaNNaNNaNNaN65.051.72NaNNaN122.9147.15748.34NaNNaNNaNNaNNaN920.12985.17
21971766.34NaNNaNNaNNaNNaNNaNNaN766.341.55NaN2.5948.34135.46313.88NaNNaNNaNNaN0.86502.681269.02
319721051.12NaNNaNNaNNaNNaNNaNNaN1051.122.1514.1325.5264.73349.791143.22NaNNaNNaNNaN0.861600.392651.50
41973854.25NaNNaNNaNNaNNaNNaNNaN854.252.2423.96130.5796.67496.391928.37NaNNaNNaNNaN0.862679.053533.30
51974674.34NaNNaNNaNNaNNaNNaNNaN674.341.7241.16110.86158.06885.611612.65NaNNaNNaNNaNNaN2810.073484.41
619751101.56NaNNaNNaNNaNNaNNaNNaN1101.562.0662.66204.5034.40566.69934.27NaNNaNNaNNaN7.011811.602913.16
719762913.27NaNNaNNaNNaNNaNNaNNaN2913.272.1511.0625.008.19264.91639.06NaNNaNNaNNaNNaN950.363863.64
819771378.48NaNNaNNaNNaNNaNNaNNaN1378.4812.0416.59244.5054.04456.10601.46NaNNaNNaNNaNNaN1384.722763.20
91978246.25NaNNaNNaNNaNNaNNaNNaN246.2515.7346.69768.90212.06552.12510.31NaNNaNNaNNaNNaN2105.802352.05
101979NaNNaNNaNNaNNaNNaNNaNNaNNaN13.0770.64398.42198.96346.36425.72NaNNaNNaNNaNNaN1453.171453.17
115YearOilNatural gasCoalHydroenergyNuclearFirewoodSugarcane and productsOther Primary_x000d_Total PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCokeCharcoalGasesOther secondaryNon-energyTotal SecundariesTotal
43201228095.71NaNNaNNaNNaNNaNNaNNaN28095.7140.2019.091713.402203.79670.228501.13NaNNaN0.41278.02241.0213667.2841762.99
44201320919.54NaNNaNNaNNaNNaNNaNNaN20919.5440.2055.041805.522327.66871.778278.32NaNNaN0.41352.74350.4314082.1035001.64
45201427214.80NaNNaNNaNNaNNaNNaNNaN27214.800.2210.991044.552448.47792.727914.45NaNNaN0.41384.97531.0013127.7740342.57
46201538639.33NaNNaNNaNNaNNaNNaNNaN38639.3318.8816.781577.762383.83650.127741.17NaNNaN0.41385.72627.6413402.3252041.64
47201644490.35NaNNaNNaNNaNNaNNaNNaN44490.3544.440.231492.372230.13704.056549.10NaNNaN0.41406.52604.2112031.4756521.82
48201754715.51NaNNaNNaNNaNNaNNaNNaN54715.5113.401.051093.922228.23580.936965.80NaNNaN0.41578.71682.8812145.3366860.85
49201856589.39NaNNaNNaNNaNNaNNaNNaN56589.390.070.551952.052788.921208.707738.33NaNNaN0.41554.04487.0814730.1571319.54
50201964490.47NaNNaNNaNNaNNaNNaNNaN64490.4717.110.332561.952433.38507.698814.11NaNNaN0.41583.07387.9515306.0079796.47
51202071982.06NaNNaNNaNNaNNaNNaNNaN71982.0633.910.122938.421109.89803.9313572.51NaNNaNNaN567.19382.2119408.1891390.24
52202165724.07NaNNaNNaNNaNNaNNaNNaN65724.073.790.122573.241064.33499.3011963.08NaNNaNNaN841.83385.7617331.4683055.53